共查询到20条相似文献，搜索用时 15 毫秒
1.
In econometrics, as a rule, the same data set is used to select the model and, conditional on the selected model, to forecast. However, one typically reports the properties of the (conditional) forecast, ignoring the fact that its properties are affected by the model selection (pretesting). This is wrong, and in this paper we show that the error can be substantial. We obtain explicit expressions for this error. To illustrate the theory we consider a regression approach to stock market forecasting, and show that the standard predictions ignoring pretesting are much less robust than naive econometrics might suggest. We also propose a forecast procedure based on the ‘neutral Laplace estimator’, which leads to an improvement over standard model selection procedures. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
2.
It is well known that some economic time series can be described by models which allow for either long memory or for occasional level shifts. In this paper we propose to examine the relative merits of these models by introducing a new model, which jointly captures the two features. We discuss representation and estimation. Using simulations, we demonstrate its forecasting ability, relative to the one‐feature models, both in terms of point forecasts and interval forecasts. We illustrate the model for daily S&P500 volatility. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
3.
This paper studies the performance of GARCH model and its modifications, using the rate of returns from the daily stock market indices of the Kuala Lumpur Stock Exchange (KLSE) including Composite Index, Tins Index, Plantations Index, Properties Index, and Finance Index. The models are stationary GARCH, unconstrained GARCH, non‐negative GARCH, GARCH‐M, exponential GARCH and integrated GARCH. The parameters of these models and variance processes are estimated jointly using the maximum likelihood method. The performance of the within‐sample estimation is diagnosed using several goodness‐of‐fit statistics. We observed that, among the models, even though exponential GARCH is not the best model in the goodness‐of‐fit statistics, it performs best in describing the often‐observed skewness in stock market indices and in out‐of‐sample (one‐step‐ahead) forecasting. The integrated GARCH, on the other hand, is the poorest model in both respects. Copyright © 1999 John Wiley & Sons, Ltd. 相似文献
4.
Stanislav Anatolyev 《Journal of forecasting》2021,40(1):94-107
The directional news impact curve (DNIC) is a relationship between returns and the probability of next period's return exceeding a certain threshold—zero in particular. Using long series of S&P500 index returns and a number of parametric models suggested in the literature, as well and flexible semiparametric models, we investigate the shape of the DNIC and forecasting abilities of these models. The semiparametric approach reveals that the DNIC has complicated shapes characterized by nonsymmetry with respect to past returns and their signs, heterogeneity across the thresholds, and changes over time. Simple parametric models often miss some important features of the DNIC, but some nevertheless exhibit superior out‐of‐sample performance. 相似文献
5.
He Jiang;Yao Dong;Jianzhou Wang; 《Journal of forecasting》2024,43(6):1859-1879
Electricity price forecasting (EPF) is an emergent research domain that focuses on forecasting the future electricity market price both deterministically and probabilistically. EPF has attracted enormous interest from both practitioners and scholars since the deregulation of the power market and wide applications of renewable energy sources, such as wind and solar energy. However, forecasting the electricity price accurately and efficiently is an extremely challenging task because of its high volatility, randomness, and fluctuation. Although quantile regression averaging (QRA) has been demonstrated to be efficacious in probabilistic EPF since the global energy forecasting competition in 2014 (GEFCom2014), it is sensitive to nuisance variables especially when the number of variables is large. The forecasting accuracy will be negatively affected by these nuisance variables. To address these challenges, this study investigates a nonconvex regularized QRA in probabilistic forecasting. Two types of nonconvex regularized QRA select the important inputs obtained from point forecasting to obtain more accurate forecasting outcomes. To demonstrate the effectiveness of the proposed EPF model, two real datasets from the European power market are considered. 相似文献
6.
Hiroyuki Kawakatsu 《Journal of forecasting》2018,37(1):83-101
This paper examines the performance of iterated and direct forecasts for the number of shares traded in high‐frequency intraday data. Constructing direct forecasts in the context of formulating volume weighted average price trading strategies requires the generation of a sequence of multistep‐ahead forecasts. I discuss nonlinear transformations to ensure nonnegative forecasts and lag length selection for generating a sequence of direct forecasts. In contrast to the literature based on low‐frequency macroeconomic data, I find that direct multiperiod forecasts can outperform iterated forecasts when the conditioning information set is dynamically updated in real time. 相似文献
7.
Han S. Der Van Knoop 《Journal of forecasting》1992,11(7):629-643
In many cases an organization makes predictions of a variable on a yearly scale although the variable is actually observed at shorter time intervals. Given such a yearly prediction, the question will arise as to under which conditions one can say that the actual development of the variable at shorter time intervals deviates so much from the year estimate as to render the latter implausible. Policy makers confronted with such a problem tend to use rather primitive statistical methods of inference. In this paper the situation is judged from a statistical point of view and placed in the context of the ‘significance test’ approach to control chart theory. It is assumed that the variables are generated by a multivariate autoregressive moving average model. Thus we derive an approximate distribution of the future observations of the series given the values of some linear compounds of the variables. With this, three control charts can be constructed. The approach is illustrated by an example based on the monthly tax returns of the Dutch central government. The example suggests the usefulness of the approach in many practical situations of forecasting and planning. 相似文献
8.
We studied the predictability of intraday stock market returns using both linear and nonlinear time series models. For the S&P 500 index we compared simple autoregressive and random walk linear models with a range of nonlinear models, including smooth transition, Markov switching, artificial neural network, nonparametric kernel regression and support vector machine models for horizons of 5, 10, 20, 30 and 60 minutes. The empirical results indicate that nonlinear models outperformed linear models on the basis of both statistical and economic criteria. Specifically, although return serial correlation receded by around 10 minutes, return predictability still persisted for up to 60 minutes according to nonlinear models, even though profitability decreases as time elapses. More flexible nonlinear models such as support vector machines and artificial neural network did not clearly outperform other nonlinear models. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
9.
Piotr Fiszeder 《Journal of forecasting》2018,37(6):641-649
In this paper we introduce a new specification of the BEKK model, where its parameters are estimated with the use of closing and additionally low and high prices. In an empirical application, we show that the use of additional information related to low and high prices in the formulation of the BEKK model improved the estimation of the covariance matrix of returns and increased the accuracy of covariance and variance forecasts based on this model, compared with using closing prices only. This analysis was performed for the following three most heavily traded currency pairs in the Forex market: EUR/USD, USD/JPY, and GBP/USD. The main result obtained in this study is robust to the applied forecast evaluation criterion. This issue is important from a practical viewpoint, because daily low and high prices are available with closing prices for most financial series. 相似文献
10.
Compared with point forecasting, interval forecasting is believed to be more effective and helpful in decision making, as it provides more information about the data generation process. Based on the well-established “linear and nonlinear” modeling framework, a hybrid model is proposed by coupling the vector error correction model (VECM) with artificial intelligence models which consider the cointegration relationship between the lower and upper bounds (Coin-AIs). VECM is first employed to fit the original time series with the residual error series modeled by Coin-AIs. Using pork price as a research sample, the empirical results statistically confirm the superiority of the proposed VECM-CoinAIs over other competing models, which include six single models and six hybrid models. This result suggests that considering the cointegration relationship is a workable direction for improving the forecast performance of the interval-valued time series. Moreover, with a reasonable data transformation process, interval forecasting is proven to be more accurate than point forecasting. 相似文献
11.
We show that contrasting results on trading volume's predictive role for short‐horizon reversals in stock returns can be reconciled by conditioning on different investor types' trading. Using unique trading data by investor type from Korea, we provide explicit evidence of three distinct mechanisms leading to contrasting outcomes: (i) informed buying—price increases accompanied by high institutional buying volume are less likely to reverse; (ii) liquidity selling—price declines accompanied by high institutional selling volume in institutional investor habitat are more likely to reverse; (iii) attention‐driven speculative buying—price increases accompanied by high individual buying‐volume in individual investor habitat are more likely to reverse. Our approach to predict which mechanism will prevail improves reversal forecasts following return shocks: An augmented contrarian strategy utilizing our ex ante formulation increases short‐horizon reversal strategy profitability by 40–70% in the US and Korean stock markets. 相似文献
12.
Haibin Xie 《Journal of forecasting》2019,38(1):11-28
An implied assumption in the asymmetric conditional autoregressive range (ACARR) model is that upward range is independent of downward range. This paper scrutinizes this assumption on a broad variety of stock indices. Instead of independence, we find significant cross‐interdependence between the upward range and the downward range. Regression test shows that the cross‐interdependence cannot be explained by leverage effect. To include the cross‐interdependence, a feedback asymmetric conditional autoregressive range (FACARR) model is proposed. Empirical studies are performed on a variety of stock indices, and the results show that the FACARR model outperforms the ACARR model with high significance for both in‐sample and out‐of‐sample forecasting. 相似文献
13.
Josephine W. C. Kwan K. Lam Mike K. P. So Philip L. H. Yu 《Journal of forecasting》2000,19(6):485-498
In this paper, we consider the price trend model in which it is assumed that the time series of a security's prices contain a stochastic trend component which remains constant on each of a sequence of time intervals, with each interval having random duration. A quasi‐maximum likelihood method is used to estimate the model parameters. Optimal one‐step‐ahead forecasts of returns are derived. The trading rule based on these forecasts is constructed and is found to bear similarity to a popular trading rule based on moving averages. When applying the methods to forecast the returns of the Hang Seng Index Futures in Hong Kong, we find that the performance of the newly developed trading rule is satisfactory. Copyright © 2000 John Wiley & Sons, Ltd. 相似文献
14.
Xiaohang Ren;Wenting Jiang;Qiang Ji;Pengxiang Zhai; 《Journal of forecasting》2024,43(7):2809-2821
In this paper, we propose a novel imaging method to forecast the daily price data of West Texas Intermediate (WTI) crude oil futures. We use convolutional neural networks (CNNs) for future price trend prediction and obtain higher prediction accuracy than other benchmark forecasting methods. The results show that images can contain more nonlinear information, which is beneficial for energy price forecasting. Nonlinear factors also have a strong influence during drastic fluctuations in crude oil prices. In the robustness tests, we find that the image-based CNN is the most stable approach and can be applied in various futures forecasting scenarios. In the prediction of low-frequency models for high-frequency data, the CNN method still retains considerable predictive power, indicating the possibility of transfer learning of our novel approach. By unleashing the power of the picture, we open up a whole new perspective for forecasting future energy trends. 相似文献
15.
The increase in oil price volatility in recent years has raised the importance of forecasting it accurately for valuing and hedging investments. The paper models and forecasts the crude oil exchange‐traded funds (ETF) volatility index, which has been used in the last years as an important alternative measure to track and analyze the volatility of future oil prices. Analysis of the oil volatility index suggests that it presents features similar to those of the daily market volatility index, such as long memory, which is modeled using well‐known heterogeneous autoregressive (HAR) specifications and new extensions that are based on net and scaled measures of oil price changes. The aim is to improve the forecasting performance of the traditional HAR models by including predictors that capture the impact of oil price changes on the economy. The performance of the new proposals and benchmarks is evaluated with the model confidence set (MCS) and the Generalized‐AutoContouR (G‐ACR) tests in terms of point forecasts and density forecasting, respectively. We find that including the leverage in the conditional mean or variance of the basic HAR model increases its predictive ability. Furthermore, when considering density forecasting, the best models are a conditional heteroskedastic HAR model that includes a scaled measure of oil price changes, and a HAR model with errors following an exponential generalized autoregressive conditional heteroskedasticity specification. In both cases, we consider a flexible distribution for the errors of the conditional heteroskedastic process. 相似文献
16.
This study reports the results of an experiment that examines (1) the effects of forecast horizon on the performance of probability forecasters, and (2) the alleged existence of an inverse expertise effect, i.e., an inverse relationship between expertise and probabilistic forecasting performance. Portfolio managers are used as forecasters with substantive expertise. Performance of this ‘expert’ group is compared to the performance of a ‘semi-expert’ group composed of other banking professionals trained in portfolio management. It is found that while both groups attain their best discrimination performances in the four-week forecast horizon, they show their worst calibration and skill performances in the 12-week forecast horizon. Also, while experts perform better in all performance measures for the one-week horizon, semi-experts achieve better calibration for the four-week horizon. It is concluded that these results may signal the existence of an inverse expertise effect that is contingent on the selected forecast horizon. 相似文献
17.
As a consequence of recent technological advances and the proliferation of algorithmic and high‐frequency trading, the cost of trading in financial markets has irrevocably changed. One important change, known as price impact, relates to how trading affects prices. Price impact represents the largest cost associated with trading. Forecasting price impact is very important as it can provide estimates of trading profits after costs and also suggest optimal execution strategies. Although several models have recently been developed which may forecast the immediate price impact of individual trades, limited work has been done to compare their relative performance. We provide a comprehensive performance evaluation of these models and test for statistically significant outperformance amongst candidate models using out‐of‐sample forecasts. We find that normalizing price impact by its average value significantly enhances the performance of traditional non‐normalized models as the normalization factor captures some of the dynamics of price impact. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
18.
Nicholas Apergis 《Journal of forecasting》2023,42(1):17-33
The goal of this paper is to use a new modelling approach to extract quantile-based oil and natural gas risk measures using quantile autoregressive distributed lag mixed-frequency data sampling (QADL-MIDAS) regression models. The analysis compares this model to a standard quantile auto-regression (QAR) model and shows that it delivers better quantile forecasts at the majority of forecasting horizons. The analysis also uses the QADL-MIDAS model to construct oil and natural gas prices risk measures proxying for uncertainty, third-moment dynamics, and the risk of extreme energy realizations. The results document that these risk measures are linked to the future evolution of energy prices, while they are linked to the future evolution of US economic growth. 相似文献
19.
Beum‐Jo Park 《Journal of forecasting》2002,21(5):381-393
Since volatility is perceived as an explicit measure of risk, financial economists have long been concerned with accurate measures and forecasts of future volatility and, undoubtedly, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used for doing so. It appears, however, from some empirical studies that the GARCH model tends to provide poor volatility forecasts in the presence of additive outliers. To overcome the forecasting limitation, this paper proposes a robust GARCH model (RGARCH) using least absolute deviation estimation and introduces a valuable estimation method from a practical point of view. Extensive Monte Carlo experiments substantiate our conjectures. As the magnitude of the outliers increases, the one‐step‐ahead forecasting performance of the RGARCH model has a more significant improvement in two forecast evaluation criteria over both the standard GARCH and random walk models. Strong evidence in favour of the RGARCH model over other competitive models is based on empirical application. By using a sample of two daily exchange rate series, we find that the out‐of‐sample volatility forecasts of the RGARCH model are apparently superior to those of other competitive models. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
20.